I built a Self-Improving Agent Loop

@jakebeau_

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Why it worked

The video provides high-value technical insight into a trending topic (AI agents) with clear, professional visualizations that make complex concepts easy to follow.

Summary

The creator demonstrates a self-improving AI agent loop he built using a local model to solve data science problems. He explains the agent's process of building, testing, and refining models based on experimental results.

Structure

1Introduction to the self-improving agent
2Explanation of the data science agent's workflow
3Visual demonstration of the experiment loop
4Discussion of the goal to build a local model agent
5Reference to research papers for further learning

Call to action

comment "agent"

On-screen text

I built a Self-Improving Agent Loop with a small local model Data Scientist Agent Data Scientist builds a model Weather Model predicts Forecast 72° Actual 58° D- SCORE updates Forecast 58° Actual 58° ✓ A+ SCORE Agent Brain: Phi 14b AGENTIC LOOP PLAN ACT OBSERVE REFLECT LOCAL 14B $0 on-device Quality 0% ITER 1 Quality 41% ITER 2 Quality 68% ITER 3 Quality 90% ITER 4 Effective harnesses for long-running agents Harness engineering: leveraging Codex in an agent-first world

Original caption

It uses a small local model as the brain (Phi 14b) #ai #agent

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Transcript

I built a self-improving agent loop with a small local model that gets better over time. I built a data science agent that solves data science competition questions. What a data scientist actually does is build a predictive model and then it runs an experiment to test the performance of that model and then it improves the model based on the findings. Each dot on here is a different experiment and the agent decides based on the results what it's going to try next. It gets decent scores but the point of this was to see if I could build a local model agent that actually gets better over time instead of going off the rails. There were two really helpful papers about how to build a long-running agent and I'll link those just comment agent.